1,960 research outputs found
Semantic Gateway as a Service architecture for IoT Interoperability
The Internet of Things (IoT) is set to occupy a substantial component of
future Internet. The IoT connects sensors and devices that record physical
observations to applications and services of the Internet. As a successor to
technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has
stumbled into vertical silos of proprietary systems, providing little or no
interoperability with similar systems. As the IoT represents future state of
the Internet, an intelligent and scalable architecture is required to provide
connectivity between these silos, enabling discovery of physical sensors and
interpretation of messages between things. This paper proposes a gateway and
Semantic Web enabled IoT architecture to provide interoperability between
systems using established communication and data standards. The Semantic
Gateway as Service (SGS) allows translation between messaging protocols such as
XMPP, CoAP and MQTT via a multi-protocol proxy architecture. Utilization of
broadly accepted specifications such as W3C's Semantic Sensor Network (SSN)
ontology for semantic annotations of sensor data provide semantic
interoperability between messages and support semantic reasoning to obtain
higher-level actionable knowledge from low-level sensor data.Comment: 16 page
How will the Internet of Things enable Augmented Personalized Health?
Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed
Challenges of Creating a Knowledge-Based Society: Education & Research for India & Gujarat
Presented at the World Gujarat Conference, Edison, NJ, August 30, 2008
Finding Street Gang Members on Twitter
Most street gang members use Twitter to intimidate others, to present
outrageous images and statements to the world, and to share recent illegal
activities. Their tweets may thus be useful to law enforcement agencies to
discover clues about recent crimes or to anticipate ones that may occur.
Finding these posts, however, requires a method to discover gang member Twitter
profiles. This is a challenging task since gang members represent a very small
population of the 320 million Twitter users. This paper studies the problem of
automatically finding gang members on Twitter. It outlines a process to curate
one of the largest sets of verifiable gang member profiles that have ever been
studied. A review of these profiles establishes differences in the language,
images, YouTube links, and emojis gang members use compared to the rest of the
Twitter population. Features from this review are used to train a series of
supervised classifiers. Our classifier achieves a promising F1 score with a low
false positive rate.Comment: 8 pages, 9 figures, 2 tables, Published as a full paper at 2016
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining (ASONAM 2016
CS 875: Semantic Web
World Wide Web (Web 1.0, or the Web, as we now know it) centers on documents and semistructured data in html, rss, and xml. The next generation Web, also called Web 2.0 and Web 3.0, has already started to emerge. Web 2.0 is about user-generated content, user participation such as through tagging, and social networking. Web 3.0, also called Semantic Web, is about labeling content such that machines can process it more intelligently and humans can exploit it more effectively. These labels or metadata add semantics (meaning) to data, and their formal representation enables powerful reasoning that leads not only to better (semantic) search but also to analysis, discovery, and decision making. Semantic Web is already a rapidly emerging field, with standards, technologies, products, and applications-as well as to excellent job prospects (for MS students) and research opportunities (for PhD students)
empathi: An ontology for Emergency Managing and Planning about Hazard Crisis
In the domain of emergency management during hazard crises, having sufficient
situational awareness information is critical. It requires capturing and
integrating information from sources such as satellite images, local sensors
and social media content generated by local people. A bold obstacle to
capturing, representing and integrating such heterogeneous and diverse
information is lack of a proper ontology which properly conceptualizes this
domain, aggregates and unifies datasets. Thus, in this paper, we introduce
empathi ontology which conceptualizes the core concepts concerning with the
domain of emergency managing and planning of hazard crises. Although empathi
has a coarse-grained view, it considers the necessary concepts and relations
being essential in this domain. This ontology is available at
https://w3id.org/empathi/
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